What model to use for fully unbalanced data?
You can balance your data-set.
Many models work with batches of samples. If you have a very unbalanced dataset, you can simply split it and ensure your batches are balanced (for example, for a Neural Network, using minibatches of 32 samples, you could draw 16 from your fraud users, and 16 from non-fraud users).
During the learning phase, this ensures the model doesn't just output the most common class, but instead tries to learn to distinguish both.